1. Field of the Invention
The present invention is directed in general to field of information processing. In one aspect, the present invention relates to a system and method for beamforming for collaborative, space division multiple access systems with transmitter and receiver antenna arrays.
2. Description of the Related Art
The demand for wireless communication systems continues to expand. Wireless communication systems transmit and receive signals within a designated electromagnetic frequency spectrum. The capacity of the electromagnetic frequency spectrum is limited. Thus, the usage expansion of wireless communication systems continually introduces challenges to improve spectrum usage efficiency. Space division multiple access (SDMA) represents one approach to improving spectrum usage efficiency. SDMA has recently emerged as a popular technique for the next generation communication systems. SDMA based methods have been adopted in several current emerging standards such as IEEE 802.16 and the 3rd Generation Partnership Project (3GPP).
FIG. 1 depicts a wireless communication system 100 that employs SDMA. The communication system 100 is a multiple-input multiple-output (MIMO) system. In MIMO systems, transmitters and receivers are both equipped with multiple antennas. The wireless communication system 100 includes multiple base stations 102.1-102.p and multiple subscriber stations 104.1-104.r, where “p” and “r” are integers representing the number of base stations and subscriber stations, respectively, in a given geographic area. Base stations and subscriber stations can be both transmitters and receivers when both base stations and subscriber stations are equipped with a receiver and a transmitter. Base stations generally communicate with multiple subscriber stations. Subscriber stations communicate directly with a base station and indirectly, via the base station, with other subscriber stations. The number of base stations depends in part on the geographic area to be served by the wireless communication system 100. Subscriber systems can be virtually any type of wireless one-way or two-way communication device such as a cellular telephones, wireless equipped computer systems, and wireless personal digital assistants. The signals communicated between base stations and subscriber stations can include voice, data, electronic mail, video, and other data, voice, and video signals.
In an SDMA-MIMO wireless communication system, each base station 102 and subscriber station 104 includes an array of antennas for transmitting and receiving signals. In SDMA, different subscriber stations share the same time-frequency channel and the separation between them occurs in the spatial dimension. During transmission, the antenna array forms a beam or multiple beams by applying a set of weights to signals applied to each antenna in the antenna array. A different set of beam forming weights is applied to communications between the base station and each subscriber station with a goal of minimizing interference between the radio communication devices signals. In some transmission schemes, such as time division duplex (TDD), beam forming between the base station and subscriber stations allows the allocation of the same frequency channel and different time channel to subscriber stations during downlink and uplink. In other transmission schemes, such as frequency division duplex (FDD), beam forming between the base station and subscriber stations allows the allocation of the same time channel and different frequency channel to subscriber stations during downlink and uplink.
FIG. 2 depicts base station 202 and subscriber stations 204.1 through 204.m in an SDMA-MIMO wireless communication system. Base station 202 represents each of base stations 102.1 through 102.p, and subscriber stations 204.1 through 204.m represent any group of m subscriber stations. MIMO systems use beamforming to transmit a single data stream through multiple antennas, and the receiver combines the received signal from the multiple receive antennas to reconstruct the transmitted data. As described in greater detail below, “beamforming” processes a signal for transmission using weight vector wi and an array of antennas to direct the signal using interference properties, and at the receiver, the received signals detected by the array of antennas are processed using a combining vector vi.
Base station 202 has an array of N antennas 206, where N is an integer greater than or equal to m. The base station prepares a transmission signal, represented by the vector xi, for each signal si, where iε{1, 2, . . . , m} (Note: lower case bold variables indicate vectors and upper case BOLD variables indicate matrices). The transmission signal vector xi is determined in accordance with Equation [1]:xi=wi·si  [1]where wi, is the ith beamforming, N dimensional transmission weight vector (also referred to as a “transmit beamformer”), and each coefficient wj of weight vector wi represents a weight and phase shift on the jth transmit antenna 206, where jε{1, 2, . . . , ki}, and ki represents the number of receiving antennas of the ith subscriber station 204.i. In addition, the term “si” is the data to be transmitted to the ith receiver. Each of the coefficients of weight vector wi may be a complex weight. Unless otherwise indicated, transmission beamforming vectors are referred to as “weight vectors,” and reception vectors are referred to as “combining vectors”.
The transmission signal vector xi is transmitted via a channel represented by a channel matrix Hi. The channel matrix Hi represents a channel gain between the transmitter antenna array 206 and the subscriber station antenna array 208.i. Thus, the channel matrix Hi can be represented by a N×Ki matrix of complex coefficients, where N is the number of antennas at the base station antenna array 206 and ki is the number of antennas in the ith subscriber station antenna array 208.i. The value of ki can be unique for each subscriber station. As will be appreciated, the channel matrix Hi can instead be represented by a ki×N matrix of complex coefficients, in which case the matrix manipulation algorithms are adjusted accordingly so that, for example, the right singular vector calculation on a N×ki channel matrix becomes a left singular vector calculation on a ki×N channel matrix. The coefficients of the channel matrix Hi depend, at least in part, on the transmission characteristics of the medium, such as air, through which a signal is transmitted. A variety of methods may be used to determine the channel matrix Hi coefficients, such as transmitting a known pilot signal to a receiver so that the receiver, knowing the pilot signal, can estimate the coefficients of the channel matrix Hi using well-known pilot estimation techniques. Alternatively, the actual channel matrix Hi is known to the receiver and may also be known to the transmitter.
At the subscriber station 204.i, the transmitted signals are received on the ki receive antennas. The received signals for the ith subscriber station 204.i are represented by a ki×1 received signal vector yi in accordance with Equation [2]:
                              y          i                =                                            s              i                        ⁢                          H              i              H                        ⁢                          w              i                                +                      (                                                            ∑                                      n                    =                    1                                    m                                ⁢                                                      s                    n                                    ⁢                                      H                    i                    H                                    ⁢                                      w                    n                                                              -                                                s                  i                                ⁢                                  H                  i                  H                                ⁢                                  w                  i                                                      )                                              [        2        ]            where “si” is the data to be transmitted to the ith subscriber station 204.i, “sn” is the data transmitted to the nth subscriber station 204.n, “HiH” represents the complex conjugate of the channel matrix correlating the base station 202 and ith subscriber station 204.i, wi is the ith base station weight vector, and wn is the nth base station weight vector. The superscript “H” is used herein as a hermitian operator to represent a complex conjugate operator. The jth element of the received signal vector yi represents the signal received on the jth antenna of subscriber station 204.i, jε{1, 2, . . . , ki}. The first term on the right hand side of Equation [2] is the desired receive signal while the summation terms less the desired receive signal represent co-channel interference.
To obtain a data signal, zi, which is an estimate of the transmitted data si, the subscriber station 204.i combines the signals received on the k antennas using a combining vector vi in accordance with Equation [3]:zi=ŝi=viHyi  [3].
In the context of the foregoing, MIMO-SDMA communication methods can be classified into two major categories: (1) collaborative and (2) non-collaborative. Collaborative MIMO-SDMA methods entail all schemes where the weighting vectors wi and combining vectors vi of base station 202 and subscriber station 204.i are designed together in a collaborative fashion. In collaborative MIMO-SDMA, each subscriber stations feeds back the downlink MIMO channel matrix to the base station, and the base station uses the knowledge of MIMO channels to all the subscriber stations 204.1-m to jointly design the weighting and combining vectors for the base station 202 and each subscriber station 204.1-m. The combining vectors are then conveyed to each subscriber station, and the communication proceeds with the base station simultaneously transmitting m data packets to each subscriber using their respective transmit weight vectors while each receiver decodes their respective packet using their respective receive weight vector. In contrast, non-collaborative systems employ a sequential design approach so that, for example, the base station 202 (or the subscriber stations 204) designs its weighting vector (or combining vector) first, and then the knowledge of the designed vectors is used to design the remaining set of vectors.
The signal throughput capacity of collaborative SDMA systems is conventionally greater than the capacity of non-collaborative systems since collaborative systems benefit from the joint knowledge of the channels Hi, iε{1, 2, . . . , m}, to all the subscriber stations 204.1-m, while a combining vector for one subscriber station 204.i in the non-collaborative systems is determined independently of the other subscriber stations 204. While the joint knowledge of the channel conditions is typically collected at the base station 202 through feedback from each subscriber station 204i, collaborative SDMA systems typically use this channel condition information to design the weight and combining vectors with the goal of reducing interference between receiving stations. On the other hand, the performance of collaborative systems can be impaired by the overhead associated with the feedback of channel estimate information and the feed forward of combining vector information, in contrast with non-collaborative system which have lower overhead requirements. Thus, conventional approaches result in the design of weight and combining vectors which do not maximize the average signal-to-noise ratio (SNR) for a receiver station that is allowed for a given channel condition. In addition, conventional approaches do not provide flexibility in terms of trading off computational requirements or the size of control information exchanged with performance. Moreover, conventional approaches require significant control information to be fed back and fed forward, thereby impairing system performance.
Accordingly, there is a need for an improved methodology for designing optimal beamforming weight vectors and combining vectors in a collaborative system. There is also a need for a collaborative MIMO-SDMA system which jointly designs the weight and combining vectors to improve the per user average SNR performance. In addition, there is a need for a family of signal processing algorithms for selecting transmit and receive array vectors for MIMO-SDMA which overcomes the problems in the art, such as outlined above. Further limitations and disadvantages of conventional processes and technologies will become apparent to one of skill in the art after reviewing the remainder of the present application with reference to the drawings and detailed description which follow.
It will be appreciated that for simplicity and clarity of illustration, elements illustrated in the drawings have not necessarily been drawn to scale. For example, the dimensions of some of the elements are exaggerated relative to other elements for purposes of promoting and improving clarity and understanding. Further, where considered appropriate, reference numerals have been repeated among the drawings to represent corresponding or analogous elements.